Sentiment Analysis-Based Categorized Opinions Expressed in Feedback Forums Using Deep Learning Technique and Message Queue Architecture

Sentiment analysis is a subfield of Natural Language Processing (NLP) which tries to process a text to extract opinions or attitudes towards topics or entities. Recently, the use of deep learning methods for sentiment analysis has received noticeable attention from researchers. Generally, different deep learning methods have shown superb performance in sentiment analysis problem. However, deep learning models are different in nature and have different strengths and limitations. For example, convolutional neural networks are useful for extracting local structures from data, while recurrent models are able to learn order dependence in sequential data. In order to combine the advantages of different deep models, in this paper we have proposed a novel approach for aspect-based sentiment analysis which utilizes deep ensemble learning. In the proposed method, we first build four deep learning models, namely CNN, LSTM, BiLSTM and GRU. Then the outputs of these models are combined using stacking ensemble approach where we have used logistic regression as meta-learner. The results of applying the proposed method on the real datasets show that our method has increased the accuracy of aspect-based prediction by 5% to 20% compared to the basic deep learning methods.

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Sentiment analysis is a type of natural language processing that categorizes text as positive or negative. It has been used in several industries in recent decades. Analyzing people’s opinions on a subject is a difficult task, because everyone’s perception is different. Sentiment analysis can be used to classify the information about people, services, and products. Every minute, a vast amount of data is uploaded to websites, blogs, and social media platforms. In this data era, new technologies and algorithms are required to extract useful information from online content. Customer expectations, market trends, and people’s attitudes are determined by sentiment analysis. It aids in the expansion of a business and making of critical production decisions. While industries utilize social media to interact with customers, businessmen use customer feedback and reviews to better their services and products. Machine learning and Deep learning models are well suited for text processing. Convolutional neural networks and Recurrent neural networks are two deep learning methods that are utilized to solve numerous challenges in the text processing sector. CNN can get the important features from the input text with pooling operation, but it is complex to get contextual data. RNN can get the contextual detail but the sequence of input words will differ. This study discusses the comparison of Convolution neural networks with LSTM algorithms.

Customer sentiment analytics has emerged as a critical tool for retail businesses aiming to understand consumer perceptions, improve customer experience, and enhance decision-making. This paper explores the application of Natural Language Processing (NLP) and deep learning techniques in analyzing unstructured textual data such as customer reviews, social media posts, and feedback to extract actionable insights for the retail industry. Traditional sentiment analysis methods often rely on keyword-based approaches, which lack the contextual understanding necessary to interpret nuanced emotions and implicit meanings. In contrast, NLP combined with advanced deep learning models, such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformers (e.g., BERT), enables more accurate sentiment classification by capturing semantic relationships, context, and sentiment polarity. The study presents a comprehensive framework that leverages data preprocessing, tokenization, embedding layers (e.g., Word2Vec, GloVe), and model training pipelines to analyze large volumes of customer-generated content. Experimental results from real-world retail datasets demonstrate the superiority of deep learning models over traditional machine learning techniques in sentiment prediction accuracy, especially in multilingual and noisy data scenarios. Moreover, the paper discusses the integration of sentiment scores with customer segmentation and product performance metrics to drive personalized marketing, inventory adjustments, and service enhancements. Challenges such as data labeling, domain adaptation, and interpretability of deep models are also addressed, along with strategies to overcome them using transfer learning, attention mechanisms, and explainable AI tools. The research concludes that customer sentiment analytics using NLP and deep learning is not merely a technological advancement but a strategic enabler for data-driven retail transformation. By systematically capturing and interpreting customer voices, retail businesses can proactively respond to market demands, enhance loyalty, and gain a competitive edge in an increasingly digital consumer landscape.

This article investigates the identification of hate speech on social media using machine learning and deep learning techniques. The research uses metrics such as F-measure, AUC-ROC, precision, accuracy, and recall assessing the effectiveness of various tactics. The findings indicate that deep learning models, particularly the bidirectional long short-term memory (BiLSTM) architecture, consistently outperform other methods in categorization tasks. The research emphasizes the importance of sophisticated neural network designs in identifying the intricacies of hostile and offensive content online. The study offers insights for promoting early identification and prevention of cyberbullying, improving secure and inclusive online environments. Future research may explore real-time detection systems, hybrid approaches, or the integration of complementary components to enhance and improve innovative technology in tackling this significant social issue. A sample tweet was annotated by specialists who categorize tweets as hate speech, offensive language, or neutral. The researchers applied shallow learning methodologies and integrated word embeddings like Word2Vec and GloVe to enhance the efficacy of deep learning models. The results indicate that BiLSTM surpasses shallow learning methods in detecting hate speech on Twitter, highlighting the efficacy of deep learning approaches in recognizing and tracking hate speech on social media platforms. When comparing different deep learning and machine learning models on different datasets, the results reveal that deep learning techniques are usually more effective. A reasonably high level of accuracy is achieved by KNN and SVM among classical algorithms, whereas Naïve Bayes performs the poorest. While deep learning approaches provide better results, tree-based models such as Random Forest and Decision Trees offer more consistent accuracy. Models based on neural networks, such as LSTM, CNN, and BI-LSTM, perform well, with LSTM-based methods excelling in particular. The most successful strategy for classification problems is the model presented, which obtains the greatest accuracy, precision, recall, F1-score of 95%. The research aids in the development of advanced tools and methodologies to mitigate hate speech on social media and foster positive online interactions. Future research may investigate alternative deep learning architectures, such as transformers, to enhance hate speech detection efficacy. The advancement of interpretable AI methodologies for identifying hate speech and delivering transparent forecasts might enhance user confidence and facilitate better content moderation decisions.

Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, etc., to MCM problems ranging from component-level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system-level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications, and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analysing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

Abstract. To Electronic health records (EHRs) are a treasure trove of patient information, including clinical notes. However, extracting meaningful insights from these unstructured narratives remains a challenge. This paper explores the potential of natural language processing (NLP) and deep learning to unlock the rich clinical narrative data and revolutionize healthcare. Clinical notes capture a wealth of information beyond structured data points. They document patient history, symptoms, treatment progress, and physician observations. Analyzing this narrative unlocks a deeper understanding of a patient's health journey, aiding in NLP can identify subtle patterns in language usage that might be indicative of specific diseases. This can assist clinicians in arriving at more accurate diagnoses, particularly for complex cases. Deep learning models can analyze narratives to identify patients at higher risk of developing certain conditions. This allows for proactive interventions and preventative measures. Extracting details about a patient's lifestyle, social context, and emotional well-being from narratives can facilitate the development of personalized treatment plans that cater to individual needs. NLP techniques like named entity recognition, sentiment analysis, and topic modeling enable the extraction of key clinical entities, emotions, and themes from narratives. Deep learning, with its ability to learn complex relationships in text data, empowers NLP tasks. Deep learning models can be trained on massive datasets of clinical notes, achieving superior performance in tasks like information extraction compared to traditional NLP methods. Deep learning models can handle the vast amount of text data generated in healthcare settings, enabling large- scale analysis of clinical narratives. By learning from diverse patient populations, deep learning models can improve their generalizability and adapt to variations in language usage within clinical documentation. Integrating NLP and deep learning into clinical workflows promises a future where the patient narrative is not just documented but actively analyzed to inform better healthcare decisions. This can lead to earlier diagnoses, more effective treatments, and ultimately, improved patient outcomes. By addressing the challenges and fostering responsible development, we can unlock the true potential of clinical narratives and empower a new era of data-driven healthcare.

In recent years, ride-hailing platforms such as Uber have significantly transformed the transportation industry by offering convenient, flexible, and technology-driven services. These platforms have made travel more accessible by enabling users to book rides instantly through mobile applications. However, customer satisfaction is not always consistent, as users frequently report issues related to pricing, driver behavior, service reliability, and application performance. Understanding these concerns is essential for improving service quality and enhancing user experience.This study focuses on analyzing customer reviews collected from Uber using sentiment analysis techniques. Since customer feedback is typically unstructured and large in volume,manual analysis is inefficient. Therefore, Natural Language Processing (NLP) techniques are used to process and interpret the textual data. The research applies both machine learning and deep learning models, including Logistic Regression, Naïve Bayes, Random Forest, and Long Short-Term Memory (LSTM), to classify reviews into positive, negative, and neutral sentiments. The dataset used in this study consists of approximately 12,000 customer reviews, which provide a diverse representation of user experiences. Data preprocessing techniques such as cleaning, tokenization, stopword removal, and stemming are applied to improve data quality. The experimental results demonstrate that deep learning models, particularly LSTM,outperform traditional machine learning approaches in terms of accuracy and contextual understanding. The proposed model achieved an overall accuracy of 90%, indicating its effectiveness in correctly classifying customer sentiments. The analysis also reveals key factors influencing customer satisfaction, including frequent ride cancellations, surge pricing, driver-related issues, and technical problems within the application. Overall, this research highlights the importance of sentiment analysis as a powerful tool for understanding customer opinions in ride-hailing platforms. The insights obtained from this study can help companies make data-driven decisions, improve service quality, and enhance customer satisfaction.

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

The rapid growth of social networks allows users to express their views on various topics, significantly impacting Sentiment Analysis (SA) in Natural Language Processing (NLP). SA extracts valuable insights and aids decision-making based on public sentiment in various domains, particularly education. In other words, Arabic Sentiment Analysis (ASA) improves educational outcomes by assessing students’ learning progress and monitoring their performance. SA faces challenges with the Arabic language due to its dialects and complex morphology. Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have advanced SA by effectively capturing relevant features, outperforming traditional Machine Learning (ML) algorithms. This paper assesses FastText word embedding with a CNN-based model for Arabic Sentiment Analysis (ASA). Our results show that the CNN model with FastText achieves strong performance in both datasets, with higher classification accuracy in the first dataset compared to the second. Which confirms the effectiveness of this proposed model to evaluate website and MOOC content by measuring learner satisfaction through forum interactions and module evaluations.

Sentiment analysis (or opinion mining), is a natural language processing (NLP) technique that harnesses Information from text data that is naturally subjective. Opinions, emotions, and attitudes are things that this information can contain. In recent years, there has been an increasing focus on sentiment analysis because of the explosive growth of unstructured data on the internet. Sentiment analysis tasks have seen state of the art performance using deep learning models. However, these models can be computationally intensive and high dimensional can produce problems of overfitting when working with relatively limited data. In order to overcome these limitations, a new hybrid model is proposed using a combination of LSTM network for feature extraction and Random Forest classifier for the final sentiment classification improving the accuracy of the sentiment analysis, while reducing computational cost. In this paper, the proposed hybrid model is designed and implemented, and evaluated on a publicly available dataset of movie reviews.

Designing of VehiNet Using Convolutional Neural Networks and Deep Learning Techniques

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data